Massive Multiple-Input Multiple-Output (MIMO) technology aims to further the diversity/multiplexing gains of wireless communication systems. Spatial modulation (SM) is a renowned low-complexity MIMO scheme that jointly uses transmitting source indices along with the data stream to convey information. However, the reliability of the spatial stream in SM is significantly influenced by the correlation between channel coefficients. Hence, applying massive-MIMO in visible light communications (VLC) remains an under-investigated area of research, due to the ill-conditioned massive-MIMO VLC channel. Augmented SM (ASM) is an approach that overcomes the channel uniqueness requirement for SM-based VLC systems. This letter adopts massive-ASM for Internet-of-Things applications with a focus on investigating ASM’s complexity and introducing different machine learning based receiver designs, including; support vector machine (SVM), logistic regression (LR), and a neural network (NN). The computational time and transmitter identification accuracy are compared and the system’s bit-error-rate performance is evaluated.

中文翻译：

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用于物联网VLC系统的基于机器学习的大规模增强空间调制（ASM）

大规模多输入多输出（MIMO）技术旨在进一步提高无线通信系统的分集/复用增益。空间调制（SM）是一种著名的低复杂度MIMO方案，该方案联合使用发送源索引和数据流来传达信息。但是，SM中空间流的可靠性受信道系数之间的相关性影响很大。因此，由于条件恶劣的大规模MIMO VLC信道，将大规模MIMO应用于可见光通信（VLC）仍然是研究不足的领域。增强SM（ASM）是一种克服基于SM的VLC系统的通道唯一性要求的方法。这封信为物联网应用采用了大规模ASM，重点是调查ASM的复杂性并介绍基于机器学习的不同接收器设计，包括：支持向量机（SVM），逻辑回归（LR）和神经网络（NN）。比较了计算时间和发射机识别精度，并评估了系统的误码率性能。